maximum-likelihood estimation problem
Updating of travel behavior parameters and estimation of vehicle trip-chain data based on plate scanning
... a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate ...reliable estimation results, the sensor location schemes for predicting trip ...
27
Maximum Likelihood Estimation of Factored Regular Deterministic Stochastic Languages
... the problem we are considering is like those addressed with Bayesian networks and Markov random fields, where com- plex probability distributions decompose into sim- pler factors (Bishop, 2006; Koller and ...
12
Estimating from Cross sectional Categorical Data Subject to Misclassification and Double Sampling: Moment based, Maximum Likelihood and Quasi Likelihood Approaches
... data problem using the misclassification ...using maximum likelihood estimation via the EM ...data problem using the misclassification probabilities, as opposed to maximum ...
34
Maximum likelihood estimation for multiscale Ornstein Uhlenbeck processes
... the problem of estimating the parameters of an Ornstein-Uhlenbeck (OU) process that is the coarse-grained limit of a multiscale system of OU pro- cesses, given data from the multiscale ...
28
Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... The mean squared error of the standard error is the sum of the variance and the square of the bias. The contribution of the bias is small. In the case reported in Table 3, the ratio of the absolute bias to the RMSE is 9% ...
26
Optimal unknown pollution source characterization in a contaminated groundwater aquifer: evaluation of a developed dedicated software tool
... The problem of unknown groundwater source identification has often been solved as an optimization ...identification problem include: non-linear maximum likelihood estimation based ...
11
Particle methods for maximum likelihood estimation in latent variable models
... integration/maximization problem; see (Doucet et ...the likelihood does not admit a closed-form expression, these artificial distributions are not standard and rely on the introduction of an increas- ing ...
16
Maximum likelihood estimation for stochastic processes - a martingale approach
... the likelihood function is a martingale. In the classical problem, that of independent and identically distributed random variables ( i ...the likelihood and the maximum likelihood ...
228
Probabilistic Low-Rank Matrix Completion from Quantized Measurements
... consider maximum likelihood (ML) estimation of M from multi-level quantized observations, and establish upper bounds on the estimation error norm for this problem, which has a faster ...
34
Manifold learning and maximum likelihood estimation for hyperbolic network embedding
... a problem for the interpretation of the network if we assume that by sharing a key, two users reciprocally endorse their trust in each other (Papadopoulos et ...
14
Maximum Likelihood Estimation of Feature Based Distributions
... Finally, we compare this proposal with Hayes and Wilson (2008). Essentially, the model here represents a “bottom-up” approach whereas theirs is “top-down.” “Top-down” models, which con- sider every set of features as ...
10
Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data
... Data Problem. The missing data problem is prevalent throughout economics (Abrevaya and Donald 2017; Breunig 2019; Fomby and Hill 1998; McDonough and Millimet 2016; Miller 2010; Wooldridge ...
27
Estimation of the Reliability Measures of a Three component System with Human Errors and Common Cause Failures
... the problem of estimating the reliability measures of a three-component identical system when the system is affected by Common Cause Shock (CCS) failures as well as human ...The maximum likelihood ...
7
Cluster Analysis of Temporal Data using Maximum Likelihood Estimation
... Clustering is the unsupervised classification of observations into groups [1]. This problem has been identified in different contexts and disciplines. This reflects the usefulness of clustering in analyzing the ...
5
Model-Free IRL Using Maximum Likelihood Estimation
... It may appear surprising that Q-averaging is able to per- form so well despite involving no optimization within the Q-update rule itself. Note however, that it simply gives a plausible value estimate of the policy in the ...
8
Maximum likelihood estimation of variance components
... the problem of estimation for non-normal data, and diagnostic tests to detect ...a problem with real-life data, and it can occur in many ways because of the complex nature of experiments analysed by ...
76
Estimation and tests for power-transformed and threshold GARCH models
... quasi-maximum likelihood estimators (QMLE) of the parameters under the condition that the error distribution has finite fourth ...deviations estimation (LADE) for PTTGARCH(p,q) model, and prove that ...
39
Estimation of Dynamic Stochastic Frontier Model using Likelihood based Approaches
... estimates are. The numbers in parentheses are the standard errors of the FML, PCL and QML estimators and computed using the inverse of the negative Hessian matrix. The numbers in brackets are the standard errors of the ...
37
Density Estimation in Infinite Dimensional Exponential Families
... inverse problem theory (see Engl, Hanke, and Neubauer, 1996), and it naturally arises here through the connection of f λ,n being a Tikhonov regularized solution to the ill-posed ...
59
Research on Initialization on EM Algorithm Based on Gaussian Mixture Model
... better parameter estimation results. What is more, its statistical meaning is easy to understand. Then, the improved initial value method can be used not only for one-dimensional Gaussian mixture model, but also ...
7